A type-3 logic fuzzy system: Optimized by a correntropy based Kalman filter with adaptive fuzzy kernel size

The number of electric vehicles and renewable energy resources integrated into the power system is increasing day by day. The objective behind the development of electric vehicles and renewable energy sources is to build a sustainable and green power system. The renewables either don’t possess system inertia or have less system inertia, therefore, they don’t effectively respond to the load variations. The battery storage system of electric vehicles is used as the first line of defense to counter the effect of load/frequency variations and make the system stable. As active power is inversely proportional to the system frequency, for this purpose electric vehicles are included in the microgrid environment. In this paper, an isolated microgrid having a reheat turbine system, wind turbine system, photovoltaic system, and electric vehicles is studied. The output of the renewables is not controlled to utilize its maximum output power. Therefore, adaptive droop control and fuzzy PI control mechanisms are implemented to cater to the frequency variations of the isolated microgrid; the former regulates the power of electric vehicles while maintaining the energy needs of each EV and the later controls the output power of reheat turbine system according to the frequency variation. Furthermore, the genetic algorithm optimization toolbox is utilized to optimize the parameters of the adaptive and fuzzy PI controllers. The proposed model is developed in MATLAB/Simulink which shows that these control techniques effectively sustained the system frequency of isolated microgrid in the desired limits.

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Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

RBFNN-based nonsingular fast terminal sliding mode control for robotic manipulators including actuator dynamics

Exponential stabilization and non-fragile sampled-date dissipative control for uncertain time-varying delay T-S fuzzy systems with state quantization

An event-triggered sliding-mode control problem is investigated for a class of multiple-input Takagi–Sugeno (T–S) fuzzy systems via designing a linear switching function. An assumption that all local linear systems share a common input matrix is removed. Then, a novel event-triggered sliding-mode controller with asynchronous premise variables is designed, which can ensure that the trajectory of multiple-input T–S fuzzy systems can be driven onto the region near the sliding surface after finite time. A sufficient condition is established to guarantee the stability of sliding motion, and the sliding-mode controller gain is obtained by solving a set of linear matrix inequalities. The positive lower bound of the interexecution time can be ensured, which means that there is no Zeno phenomenon. In the end, the advantages and effectiveness of the theoretical results are illustrated by two examples.

Jiahui Wang

Papers

Adaptive sliding mode fault-tolerant control for type-2 fuzzy systems with distributed delays